[Eeglablist] Rank deficiency problem with ICA run on re-referenced data to the average of the temporal channels

[Isil Bilgin]

Dear Scott,

thank you very much for your detailed explanations, they really help.

Since I collected my data in a simultaneous EEG/fMRI experiment and TP9 and
TP10 is very noisy (due to the position of the head during the data
collection), so I had a bit of struggle to make sure that I clean them
enough before re-referencing however it didn't feel right given the signals
from those channels modified and maybe not reflecting the true signal
anymore. I am really happy to hear average referencing is a good way
forward for the source localisation aspect. I was aiming to use scalp
channels with the BEM head modelling for the source localisations, so hope
these methods still alright with using the average referencing. It's just
the worry of using average referencing is less common used in N400 research
but mainly mastoid channels, therefor eI have been worrying whether I would
lose the comparability of the study with the N400 literature. But given the
noise in those channels, I guess I will have no other options but average
referencing I believe.

Dear Makoto, thank you very much for sharing the tip, I didn't know such a
feature exists, I will try and see if I get any rank deficiency error due
to some other processing steps that I am doing atm, such as channel
interpolation and high pass filtering. Thank you so much.

Kind regards,

Isil


On Fri, May 7, 2021 at 2:46 AM Scott Makeig <smakeig at gmail.com> wrote:

> Isil -
>
> The problem is  that TP9 referenced to (TP9+TP10)/2 gives TP9/2 - TP10/2
>  whereas TP10 referenced to (TP9+TP10)/2 gives TP10/2 - TP9/2
> These two new channels are just opposites of each other...
> so they do not both contribute 1 to the rank.
>
> I would recommend using average reference. Any referencing scheme amounts
> to applying a spatial filter, and average reference can be the most stable
> (under
> certain assumptions). It is used to perform source localization, for
> example.
>
> If you want to analyze the individual scalp channels after referencing
> (rather than
> their independent component signals, which I would recommend), there are
> average reference schemes that attempt to improve the stability based on an
> electrical head model (though not an individual head model and still
> making some
> assumptions) - one has an EEGLAB plug-in, I believe.
>
> One beauty of ICA is that the brain sources it reveals are reference free
> --
> that is, their activities do not depend on the choice of common reference.
> Their
> scalp projections (scalp maps) do, of course - but their implied source
> locations do not!
>
> The scalp channel data are not the data of interest for neuroscience - the
> source data are!
>
> Scott Makeig
>
> On Thu, May 6, 2021 at 9:32 PM Isil Bilgin via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
>> Dear EEGLAB members,
>>
>> I am running an ICA on my EEG data collected with 64 channels (63 + 1 ECG)
>> but I am getting a message that says "EEGLAB has detected that the rank of
>> your data matrix is lower the number of input data channels. This might be
>> because you are including a reference channel or because you are running a
>> second ICA decomposition. The proposed dimension for ICA is 62 (out of 63
>> channels). Rank computation may be inaccurate so you may edit this number
>> below. If you do not understand simply press OK." with a text box asking
>> for the input for the proposed rank that is written 62 on it currently.
>> And
>> when I run the ICA with this default, I get 62 ICA components as expected.
>>
>>
>> I know from previous messages here
>> https://sccn.ucsd.edu/pipermail/eeglablist/2013/007062.html and Makoto's
>> explanation here
>> https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline
>> there are several reasons that might cause this. In order to find out I
>> run
>> ICA each time separately for each preprocessing steps and I found out
>> re-referencing the data to TP9 and TP10 channels (for the further N400
>> analysis as recommended in the literature) results in such rank
>> deficiency.
>>
>> So I was wondering would it be a problem in the future for the source
>> localisation of the data or ERP estimations and if does how can I avoid
>> the
>> problem occurring?
>>
>> I would really appreciate any suggestions on this problem, please.
>>
>> Kind regards,
>>
>> Isil Bilgin
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>
>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for
> Computational Neuroscience, Institute for Neural Computation, University of
> California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott
>